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Self-Derived Wavelet Compression and Self Matching Reconstruction Algorithm for Environmental Data in Complex Space of Coal Mine Roadway

Author

Listed:
  • Xusheng Xue

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710064, China)

  • Chuanwei Wang

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710064, China)

  • Hongwei Ma

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710064, China)

  • Qinghua Mao

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710064, China)

  • Xiangang Cao

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710064, China)

  • Xuhui Zhang

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710064, China)

  • Guangming Zhang

    (General Engineering Research Institute, Liverpool John Moores University, Merseyside L3 5UX, UK)

Abstract

A crucial assurance for coal mine safety production, prevention and control, and rescue, which is the fundamental tenet of implementing intelligent coal mining, is the safety, stability, and quick transmission of coal mine roadways. However, because of the complex structure of the roadway environment, such as limited and variable space and numerous pieces of equipment, the wireless communication network is affected by the environment, the data transmission channel characteristics are complex and variable, and the existing data transmission methods are weak in adapting to the changing channel. These factors result in poor stability of the transmission of coal mine roadway environment detection data in the wireless communicative network. As a result, this article investigates the wireless communication systems’ real-time transmission in the intricate environmental setting of a coal mine. Based on the application of multiscale wavelet theory in data compression and reconstruction, an adaptive multiscale wavelet compression model based on the wireless data transmission channel is proposed, with an improved Huffman data compression coding algorithm derived from the multiscale wavelet, so that the environmental data meet the wireless communication channel transmission capability. The proposed algorithm boosts the compression ratio and adaptability of environmental data. A self-matching wavelet reconstruction algorithm is developed to achieve real-time and accurate data reconstruction following self-driven wavelet decomposition. The compression and reconstruction experiment performed during real-time wireless transmission of gas concentration data reveals that the original signal’s compression ratio reaches 74% with minor error and high fidelity. The algorithm provides the theoretical foundation for compression and reconstruction in complex coal mine environments for accurate, stable, and real-time data transmission. It is critical for ensuring reliable data transmission in safe production, prevention and control, rescue, and other operations, and it provides theoretical and technical support for intelligent coal mining.

Suggested Citation

  • Xusheng Xue & Chuanwei Wang & Hongwei Ma & Qinghua Mao & Xiangang Cao & Xuhui Zhang & Guangming Zhang, 2022. "Self-Derived Wavelet Compression and Self Matching Reconstruction Algorithm for Environmental Data in Complex Space of Coal Mine Roadway," Energies, MDPI, vol. 15(20), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7505-:d:939955
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    Citations

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    Cited by:

    1. Alexander Vitalevich Martirosyan & Yury Valerievich Ilyushin, 2022. "The Development of the Toxic and Flammable Gases Concentration Monitoring System for Coalmines," Energies, MDPI, vol. 15(23), pages 1-13, November.
    2. Sergey Sidorenko & Vyacheslav Trushnikov & Andrey Sidorenko, 2024. "Methane Emission Estimation Tools as a Basis for Sustainable Underground Mining of Gas-Bearing Coal Seams," Sustainability, MDPI, vol. 16(8), pages 1-22, April.

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